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- Background or exposure to data science / machine learning / AI approaches and software tools
- Knowledge of statistical modelling of processes and/or control sciences
- Team working and communication skills
- Knowledge of German or similar languages is not required but is a plus
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Doctoral researcher in Dynamic stochastic learning of train dynamics as enabler to highly automated train operation - Zürich, Schweiz - ETH
Beschreibung
Job description
We expect the following tasks to be performed:
The ultimate objective is to be both precise /accurate, parsimonious, and useful for decision making. Moreover there is a lot of expert knowledge to be considered at multiple levels, both as input of the model specification, and as output of the model estimation. The process, data and output should determine standards and specification for both human driver and automated train operations, and trajectory planning for any downstream optimization of the schedule, for both passenger trains, and especially for freight trains (which have very variable composition, weight, length).
Your profile
Ideally, you have (or are about to receive) a Masters' degree in transport sciences, quantitative finance, data science, econometrics, management/ decision sciences mathematics, statistics, computer science, physics or related fields.
Your research track is consistent and shows a track record, or clear potential, for modelling, control, and optimisation of transport systems. You are highly motivated and self-driven, with a clear research vision, academic ambition, and excellent communication and writing skills (fluent spoken and written English is mandatory). Moreover, the following skills are expected of a promising candidate:
You enjoy working in an interactive international environment with doctoral students, post-docs and senior scientists, referring continuously to practical problems and solutions.
